Hybrid Quantum Recurrent Neural Network For Remaining Useful Life Prediction

• URL: http://arxiv.org/abs/2504.20823v1
• Date: Tue, 29 Apr 2025 14:41:41 GMT
• Title: Hybrid Quantum Recurrent Neural Network For Remaining Useful Life Prediction
• Authors: Olga Tsurkan, Aleksandra Konstantinova, Aleksandr Sedykh, Dmitrii Zhiganov, Arsenii Senokosov, Daniil Tarpanov, Matvei Anoshin, Leonid Fedichkin,
• Abstract summary: We introduce a Hybrid Quantum Recurrent Neural Network framework, combining Quantum Long Short-Term Memory layers with classical dense layers for Remaining Useful Life forecasting.<n> Experimental results demonstrate that, despite having fewer trainable parameters, the Hybrid Quantum Recurrent Neural Network achieves up to a 5% improvement over a Recurrent Neural Network.
• Score: 67.410870290301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predictive maintenance in aerospace heavily relies on accurate estimation of the remaining useful life of jet engines. In this paper, we introduce a Hybrid Quantum Recurrent Neural Network framework, combining Quantum Long Short-Term Memory layers with classical dense layers for Remaining Useful Life forecasting on NASA's Commercial Modular Aero-Propulsion System Simulation dataset. Each Quantum Long Short-Term Memory gate replaces conventional linear transformations with Quantum Depth-Infused circuits, allowing the network to learn high-frequency components more effectively. Experimental results demonstrate that, despite having fewer trainable parameters, the Hybrid Quantum Recurrent Neural Network achieves up to a 5% improvement over a Recurrent Neural Network based on stacked Long Short-Term Memory layers in terms of mean root mean squared error and mean absolute error. Moreover, a thorough comparison of our method with established techniques, including Random Forest, Convolutional Neural Network, and Multilayer Perceptron, demonstrates that our approach, which achieves a Root Mean Squared Error of 15.46, surpasses these baselines by approximately 13.68%, 16.21%, and 7.87%, respectively. Nevertheless, it remains outperformed by certain advanced joint architectures. Our findings highlight the potential of hybrid quantum-classical approaches for robust time-series forecasting under limited data conditions, offering new avenues for enhancing reliability in predictive maintenance tasks.

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